@inproceedings{e81571c7e8d045d38e7c333a2343b9bf,
title = "Context over time: Modeling context evolution in social media",
abstract = "The rise of online social media has led to an explosion in user-generated content. However, user-generated content is difficult to analyze in isolation from its context. Accordingly, context detection and tracking its evolution is essential to understanding social media. This paper presents a statistical model that can detect interpretable topics along with their contexts. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags that frequently occur with a topic. The model combines a context with a related topic by jointly modeling words with hashtags and time. Experiments on real datasets demonstrate that the proposed model successfully discovers both meaningful topics and contexts, and tracks their evolution.",
keywords = "Context and topic evolution, Social media, Topic model",
author = "Alam, {Md Hijbul} and Ryu, {Woo Jong} and Sang-Geun Lee",
note = "Publisher Copyright: Copyright {\textcopyright} 2014 by the Association for Computing Machinery, Inc. (ACM).; 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD 2014, Co-located with 23rd ACM Conference on Information and Knowledge Management, CIKM 2014 ; Conference date: 03-11-2014",
year = "2014",
month = nov,
day = "3",
doi = "10.1145/2665994.2665996",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
number = "November",
pages = "15--18",
booktitle = "DUBMOD 2014 - Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, co-located with CIKM 2014",
edition = "November",
}